Automotive Science and Engineering

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    Modeling and identification of the system of Iranian cars is one of the most basic needs of automotive and consumer groups and has a broad role for safe driving. It has happened with speed increasing or changing of shift gear, effects on water temperature or the car's torque has been observed, but how much and how intensely and with what algorithm this effect is identifiable, can be modeled and controlled, because up to now an algorithm that can show these effects during driving has not existed that what reaction should be made by the vehicle when it occurs untimely.
    Identification of each automobile sector lonely has been considered in recent decades, and in some cases, some relationships have been investigated, but from a control point of view, the lack of comprehensive effects of all parts of a car on the other parts is to get an identification algorithm in the automotive industry, and it requires more in-depth studies, because the complexity of the behavior of different parts of the car has made many attempts not fully understandable. Hear it's supposed to control different parameters of Iranian vehicles by using LS estimation and fuzzy logic controller and the simulation is done in Matlab software by storing and validating data of a Dena vehicle through CAN network.

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In this paper an idea for hybridization of conventional vehicles has proposed. The case study performed on one of the common vehicles on country roads i.e. Samand. This vehicle has high production volume but low fuel performance therefore hybridization of it could be attractive for its manufacture. This paper aims that the hybridization idea and its structure to need minimum mechanical modifications. In consequence attractiveness of this idea for industry could be high. A cost optimization has been performed for sizing of additional components such as electric motors and battery modules and the simulation results has been adopted to verify the proposed idea for case study with hybrid simulation of GT-Suit and MATLAB softwares.      

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The control of Antilock Braking Systems (ABS) is a difficult problem, because of their nonlinearities and uncertainties appearing in their dynamics and parameters. To overcome these issues, this paper proposes a new adaptive controller for the next generation of ABS. After considering a complex vehicle dynamic, a triple adaptive fuzzy control system is presented. Important parameters of the vehicle dynamic include two separated brake torques for front ands rear wheels, as well as longitudinal weight transfer which is caused by the acceleration or deceleration. Because of the nonlinearity of the vehicle dynamic model, three fuzzy-estimators have been suggested to eliminate nonlinear terms of the front and rear wheels’ dynamic. Since the vehicle model parameters change due to variations of road conditions, an adaptive law of the controller is derived based on Lyapunov theory to adapt the fuzzy-estimators and stabilize the entire system. The performance of the proposed controller is evaluated by some simulations on the ABS system. The results demonstrate the effectiveness of the proposed method for ABS under different road conditions.

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Due to the increasing level of air pollution and the reduction of fossil fuels, the need for new technologies and alternative fuels is felt more than ever. Proton exchange membrane fuel cells (PEMFCs) are one of these technologies, which have been of great interest to the researchers due to the benefits of non-contamination, high efficiency, fast start-up, and high power density. The proper functioning of the fuel cell requires thermal management and water management within the cells. To this end, in this work, the effect of different parameters on the performance of PEM fuel cell was investigated. The results demonstrated that the performance of the cell increases with increasing the pressure in the low current densities, while in the high current density, performance decreases with increasing the pressure of the cell. Also, the study of the effect of relative humidity shows that increasing the relative humidity of the cathode does not have much effect on the performance of the cell while increasing the relative humidity of the anode improves the performance of the cell.

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Traffic control is a major and common problem in large-scale urban decision-making, particularly in metropolises. Several models of intelligent highways have been proposed to tackle the issue, and the longitudinal speed control of vehicles remains a key issue in the field of intelligent highways. Many researchers have been investigating the longitudinal speed control of vehicles. However, their proposed models disregard important and influential presumptions. In the present study, the longitudinal dynamics control of vehicles in the presence of nonlinear factors, such as air resistance, rolling resistance, a not ideal gearbox, an internal combustion engine and a torque converter, is investigated. Moreover, considering the presented model and using model reference adaptive control, a proper controller is designed to control the longitudinal speed of intelligent vehicles. The results of the proposed model, which is validated by commercial software, are in good agreement with real-world situations. Hence, a positive step is taken for controlling longitudinal speed of intelligent vehicles on an intelligent highway platform.

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Rollover of sport utility vehicles is a critical challenge for dynamic stability of the vehicle. Due to the high rate of fatalities resulted from the rollover, in order to reduces the injuries, the design of active vehicle controllers has received significant attention among the researchers and car companies. In this article, a multi-criteria optimum method is discussed in order to design a dynamics stabilizing controller via differential braking with an optimum braking torque distribution. To this end, the nonlinear control method on the basis of the sliding mode techniques has been implemented that provides ride comfort, improve safety performance, and maintain maneuverability. To address the trade-off between the conflicting requirements of vehicle dynamic control in terms of maneuverability and rollover prevention capability, we formulate an artificial intelligence-based multi-criteria genetic algorithms. The simulation verification analysis indicates that the utilized optimum distribution braking torques result in the desired enhancement in roll stability of the vehicle.

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Reducing the fuel consumption and energy use in transportation systems are the active research areas in recent years. This paper considers the repetitive mission of the intercity passenger buses as a case for fuel reduction. A look-ahead energy management system is proposed which uses the information about the geometry and speed limits of the road ahead. This data can be extracted using road slope and speed limits database in combination with a GPS unit. A fuzzy gain scheduling algorithm is proposed to improve the performance of the look-ahead control. The road slope and speed limit specifications called road pattern can define some two dimensional regions. The main parameters of the proposed fuzzy look-ahead controller are optimized in each region using the genetic algorithm.  The final output of the proposed controller is the desired speed that regularly is fed to the conventional cruise controller with new set points. The simulation results of the proposed energy management system show that the fuel consumption is significantly reduced.

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In this paper, an adaptive cruise control system is designed that is controlled by a neural network model. This neural network model is trained with data resulting from the simulation of a multi-objective nonlinear predictive adaptive cruise control system. For this purpose, first, an adaptive cruise control system was designed using the concept of model predictive control based on a nonlinear model to maintain the desired speed of the driver, maintain a safe distance with the car in front, reducing fuel consumption and increasing ride comfort. Due to the time-consuming computations in predictive control systems and the consequent need for powerful and expensive hardware, it was decided to use the extracted data from the simulation of this designed cruise control system to train a neural network model and use this model to achieve control objectives instead of the predictive controller. Using the neural network model in the cruise control system, despite a significant reduction in computation time, the control objectives were well achieved, and in fact a combination of model predictive controller accuracy and neural network controller speed was used.

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This paper experimentally investigates the trafficability of a small tracked vehicle on a slope. An increase in the angle of slope inclination may divert the vehicle from its path. In other words, the deviation of the vehicle is due to a sudden increase in the yaw angle. Also, the tip-over occurs at a specific slope angle. The locomotion of the small tracked vehicle on soils with different terramechanics (such as cohesion, internal friction angle, cohesive modulus, and friction modulus) is also simulated to evaluate its slope-traversing performance. Moreover, the impact of velocity and soil type on traversing a slope is measured. The proposed yaw angle control system is modeled for controlling the yaw angle of the tracked vehicle. This controller is designed through co-simulation. It keeps the tracked vehicle at zero yaw angle to achieve straight locomotion on slopes. It is compared to the PI, PID, and fuzzy controllers. The response of this controller is faster than PI and PID controllers. A Comparison between fuzzy and proposed yaw angle controller yields almost similar responses. The mechanism of the proposed yaw angle controller is also easier to understand. The precision of the controller's performance is measured by simulating over different terrains.

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In this paper, firstly chaotic behavior of the lateral dynamics of vehicle is investigated by the use of numerical tools including Lyapunov exponent and bifurcation diagrams. To this end rout to chaos along with period doubling and quasi-periodic responses are demonstrated in terms of bifurcation diagrams. After chaos analysis, a novel controller commensurate with the chaotic characteristics of the system, in conformity with Poincaré map is represented to suppress the chaotic behavior of lateral movement. The Poincaré map of the system is derived by means of a neuro fuzzy network. A robust Fuzzy system on the basis of nonlinear Ott-Grebogi-Yorke (OGY) method forms the control system. Closed-loop results of the system shows effectiveness of the chaos controller in extreme conditions.

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Environmental pollution and reduction of fossil fuel resources can be considered as the most important challenges for human society in the recent years. The results of previous studies show that the main consumer of fossil fuels and, consequently, most of the air pollutants, is related to the transportation industry and especially cars. The increasing growth of vehicles, the increase in traffic and the decrease in the average speed of inner-city vehicles have led to a sharp increase in fuel consumption. To address this problem, automakers have proposed the development and commercialization of hybrid vehicles as an alternative to internal combustion vehicles. In this paper, the design of an energy management system in a fuel-cell hybrid vehicle based on the look-ahead fuzzy control is considered. The preparation of fuzzy rules and the design of membership functions is based on the fuel efficiency curve of the fuel-cell. In look-ahead fuzzy control, the ahead conditions of the vehicle are the basis for decision in terms of slope and speed limit due to path curves as well as battery charge level. The fuzzy controller will determine the on or off status of the fuel-cell, as well as the power required. The motion of the fuel-cell hybrid vehicle on a real road is simulated and the performance of the proposed look-ahead controller is compared with the base controller (thermostatic method). The simulation results show that using the proposed approach can reduce the fuel consumption of the fuel-cell hybrid vehicle as well as travel time.

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In this paper, analysis and control of the chaotic vibrations in bounce dynamic of vehicle have been studied according to the comparison of controller based on the nonlinear control and chaos controller on the basis of the chaotic system properties. After modeling the vehicle dynamic, the chaotic behavior of the uncontrolled system was determined using combination of the numerical analysis including bifurcation diagrams and max Lyapunov exponent. The system parameters values were then identified in the quasi-periodic and chaotic behavior system. In order to eliminate the chaotic vibrations, the control signal was first developed using a nonlinear fast-terminal sliding mode control algorithm that its control gains are estimated online by fuzzy logic which was designed for vehicle vertical dynamics. Then the delayed feedback control was designed based on the development of Pyragas algorithm to control the system based on the properties of the chaotic system and generation of a small control signal. Comparison of the feedback system depicts priority of the Fuzzy-Pyragas controller in less energy consumption and better behavior.

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Electric vehicles are attaining significant attention recently and the current legislation is forcing the automotive industry to electrify the productions. Regardless of electric energy accumulation technology, drive technology is one of the vital components of EVs. The motor drive technology has been mainly developed based on the application which required position/velocity control. In automotive application, however, torque control is an important aspect since the drivers have already used to drive the vehicle based on torque control approach in traditional powertrain system. In this article, a model-based approach is employed to develop a controller which can guarantee the precise control of the induction motors torque for a micro electric vehicle (EV) application regardless of operating conditions. The implementation of the control drive was conducted in MATLAB/Simulink environment, followed by Model In the Loop simulation and testing at various test conditions to confirm the robustness of the developed drive. Direct Torque Control (DTC) with optimum voltage vector selection method is employed to control the motor torque that requires fewer power electronics to process its operation and hence lowers the cost of implementation. The result shows the practicality of the designed control system and its ability to track reference torque commands. Vitally, the controlled approach shows fair abilities to control IMs to produce torque at both the motoring and regenerative modes which is a highly important requirement in electrical propulsion powertrains. Furthermore, the controller’s response time was within the industrial standard range which confirms its suitability for industrial implementation at low cost.

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In this study, an adaptive sliding mode controller (ASMC) based on estimation of tire-road friction coefficient is proposed for engagement control of automotive dry clutch. The control of clutch engagement is one of the most important parts of gear-shift process for automated manual transmission. Accurate amount of drive shaft torque in modelling of powertrain system is essential to guarantee smooth engagement of the clutch and rapid response of the control system. As the tire-road friction coefficient has significant influence on drive shaft torque, an estimator is designed to calculate this parameter. The ASMC is proposed for the clutch control to overcome the system uncertainties and a proportional integral (PI) controller is adopted to engine speed control. In addition, a nonlinear estimator utilizing unscented Kalman filter is applied to estimate the state variables that are measured hardly such as wheel slip and longitudinal vehicle velocity. The simulation results demonstrate the high effectiveness of the combined use of presented controller and road friction coefficient estimator for improving the smooth clutch engagement in comparison to the control system without estimator.

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In this article, the optimal robust H₂ / H_∞ control of self-driving car platoons (SDCPs) under external disturbance is investigated. By considering the engine dynamics and the effects of external disturbance, a linear dynamical model is presented to define the motion of each self-driving car (SDC). Each following SDC is in direct communication with the leader. By utilizing the relative position of following SDCs and the leader, the error dynamics of each SDC is calculated. The particle swarm optimization (PSO) method is utilized to find the optimal control gains. To this aim, a cost function which is a linear combination of H₂ and H_∞ norms of the transfer function between disturbance and target variables is constructed. By employing the PSO method, the cost function will be minimized and therefore, the robustness of the controller against external disturbance is guaranteed. It will be proved that under the presented robust control method, the negative effects of disturbance on system performance will significantly reduce. Therefore, the SDCP is internally stable and subsequently, each SDC tracks the motion of the leader. In order to validate the proposed method, simulation examples will be presented and analyzed.

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This study examines the impact of a fuzzy logic-based control strategy on managing peak power consumption in the auxiliary power unit (APU) of a hybrid electric bus. The APU comprises three components: an air compressor, a power steering system, and an air conditioning system (AC) connected to an electric motor. Initially, these components were simulated in MATLAB-SIMULINK software. While the first two were deemed dependent and independent of vehicle speed, respectively, the stochastic behavior of the steering was emulated using the Monte Carlo method. Subsequently, a fuzzy controller was designed and incorporated into the APU to prevent simultaneous operation of the three accessories as much as possible. The results of repeated simulations demonstrated that the designed fuzzy controller effectively distributed the operation of the accessories throughout the driving cycle, thereby reducing overlaps in auxiliary loads. Consequently, the APU's average and maximum power consumption exhibited significant reductions. Furthermore, through multiple simulations with an upgraded power system model integrating the new APU-controller package, it was established that the proposed strategy for managing auxiliary loads in the bus led to lower fuel consumption and emissions.

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Fleet routing is one of the basic solutions to meet the good demand of customers in which decisions are made based on the limitations of product supply warehouses, time limits for sending orders, variety of products and the capacity of fleet vehicles. Although valuable efforts have been made so far in modeling and solving the fleet routing problem, there is still a need for new solutions to further make the model more realistic. In most research, the goal is to reach the shortest distance to supply the desired products. Time window restrictions are also applied with the aim of reducing product delivery time. In this paper, issues such as customers' need for multiple products, limited warehouses in terms of the type and number of products that can be offered, and also the uncertainty about handling a customer's request or the possibility of canceling a customer order are considered. We used the random model method to deal with the uncertainty of customer demand. A fuzzy clustering method was also proposed for customer grouping. The final model is an integer linear optimization model that is solved with the powerful tools of Mosek and Yalmip. Based on the simulation results, it was identified to what extent possible and accidental changes in customer behavior could affect shipping costs. It was also determined based on these results that the effective parameters in product distribution, such as vehicle speed, can be effective in the face of uncertainty in customer demand.

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The turn-around task is one of the challenging maneuvers in automated driving which requires intricate decision making, planning and control, concomitantly. During automatic turn-around maneuver, the path curvature is too large which makes the constraints of the system severely restrain the path tracking performance. This paper highlights the path planning and control design for single and multi-point turn of autonomous vehicles. The preliminaries of the turn-around task including environment, vehicle modeling, and equipment are described. Then, a predictive approach is proposed for planning and control of the vehicle. In this approach, by taking the observation of the road and vehicle conditions into account and considering the actuator constraints in cost function, a decision is made regarding the minimum number of steering to execute turn-around. The constraints are imposed on the speed, steering angle, and their rates. Moreover, the collision avoidance with road boundaries is developed based on the GJK algorithm. According to the simulation results, the proposed system adopts the minimum number of appropriate steering commands while incorporating the constraints of the actuators and avoiding collisions. The findings demonstrate the good performance of the proposed approach in both path design and tracking for single- and multi-point turns.